Developing the Profiles of Business Analytics Adopters and Non-adopters Using Data Mining Tools

References

• Min H. Global business analytics models: concepts and applications in predictive, healthcare, supply chain, and finance analytics. Saddle River (New Jersey): Pearson Education; 2016.
   Google Scholar
• Hindle GA, Vidgen R. Developing a business analytics methodology: a case study in the foodbank sector. Eur J Oper Res. 2018;268(3):836–51. doi:10.1016/j.ejor.2017.06.031.
   Web of Science ®Google Scholar
• Holsapple C, Lee-Post A, Pakath R. A unified foundation for business analytics. Decis Support Syst. 2014;64:130–41. doi:10.1016/j.dss.2014.05.013.
   Web of Science ®Google Scholar
• Ajah IA, Nweke HF. Big data and business analytics: trends, platforms, success factors and applications. Big Data Cogn. Comput 2019;3(2):32–62. doi:10.3390/bdcc3020032.
   Google Scholar
• Klatt T, Schlaefke M, Moeller K. Integrating business analytics into strategic planning for better performance. J Bus Strategy. 2011;32(6):30–39. doi:10.1108/02756661111180113.
   Google Scholar
• Trkman P, McCormack K, De Oliveira MPV, Ladeira MB. The impact of business analytics on supply chain performance. Decis Support Syst. 2010;49(3):318–27. doi:10.1016/j.dss.2010.03.007.
   Web of Science ®Google Scholar
• Akter S, Wamba SF, Gunasekaran A, Dubey R, Childe SJ. How to improve firm performance using big data analytics capability and business strategy alignment? Int. J. Prod. Econ 2016;182:113–31. doi:10.1016/j.ijpe.2016.08.018.
   Web of Science ®Google Scholar
• Akter S, Gunasekaran A, Wamba SF, Babu MM, Hani U. Reshaping competitive advantages with analytics capabilities in service systems. Technol Forecast Soc Change. 2020a;159:120180. doi:10.1016/j.techfore.2020.120180.
   Web of Science ®Google Scholar
• Akter S, Motamarri S, Hani U, Shams R, Fernando M, Babu MM, Shen KN. Building dynamic service analytics capabilities for the digital marketplace. J Bus Res. 2020b;118:177–88. doi:10.1016/j.jbusres.2020.06.016.
   Web of Science ®Google Scholar
• Aydiner AS, Tatoglu E, Bayraktar E, Zaim S, Delen D. Business analytics and firm performance: the mediating role of business process performance. J Bus Res. 2019;96:228–37. doi:10.1016/j.jbusres.2018.11.028.
   Web of Science ®Google Scholar
• Chae B, Olson DL. Business analytics for supply chain: a dynamic-capabilities framework. Int J Inf Technol Decis Mak. 2013;12(1):9–26. doi:10.1142/S0219622013500016.
   Web of Science ®Google Scholar
• Davenport TH, Harris JG. What people want (and how to predict it). MIT Sloan Manag. Rev 2009;50:22–33.
   Web of Science ®Google Scholar
• De Oliveira MPV, McCormack K, Trkman P. Business analytics in supply chains–The contingent effect of business process maturity. Expert Syst Appl. 2012;39(5):5488–98. doi:10.1016/j.eswa.2011.11.073.
   Web of Science ®Google Scholar
• Dubey R, Gunasekaran A, Childe SJ, Blome C, Papadopoulos T. Big data and predictive analytics and manufacturing performance: integrating institutional theory, resource‐based view and big data culture. Br. J. Manag 2019;30(2):341–61. doi:10.1111/1467-8551.12355.
   Web of Science ®Google Scholar
• Popovič A, Hackney R, Tassabehji R, Castelli M. The impact of big data analytics on firms’ high value business performance. Inf. Syst. Front 2018;20(2):209–22. doi:10.1007/s10796-016-9720-4.
   Web of Science ®Google Scholar
• Ramanathan R, Philpott E, Duan Y, Cao G. Adoption of business analytics and impact on performance: a qualitative study in retail. Prod. Plan. Control 2017;28(11–12):985–98. doi:10.1080/09537287.2017.1336800.
   Web of Science ®Google Scholar
• Srinivasan R, Swink M. An investigation of visibility and flexibility as complements to supply chain analytics: an organizational information processing theory perspective. Prod. Oper. Manag 2018;27(10):1849–67. doi:10.1111/poms.12746.
   Web of Science ®Google Scholar
• Vidgen R, Shaw S, Grant DB. Management challenges in creating value from business analytics. Eur J Oper Res. 2017;261(2):626–39. doi:10.1016/j.ejor.2017.02.023.
   Web of Science ®Google Scholar
• Mikalef P, Pappas IO, Krogstie J, Giannakos M. Big data analytics capabilities: a systematic literature review and research agenda. Inf. Syst. E-Bus. Manag 2018;16(3):547–78. doi:10.1007/s10257-017-0362-y.
   Web of Science ®Google Scholar
• Chen DQ, Preston DS, Swink M. How the use of big data analytics affects value creation in supply chain management. J. Manag. Inf. Syst 2015;32(4):4–39. doi:10.1080/07421222.2015.1138364.
   Web of Science ®Google Scholar
• Ghasemaghaei M, Hassanein K, Turel O. Hassanein, K., Turel, O. Increasing firm agility through the use of data analytics: the role of fit. Decis Support Syst. 2017;101:95–105. doi:10.1016/j.dss.2017.06.004.
   Web of Science ®Google Scholar
• Yin J, Fernandez V. A systematic review on business analytics. J. Ind. Eng. Manag 2020;13(2):283–95. doi:10.3926/jiem.3030.
   Google Scholar
• Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989;13(3):319–39. doi:10.2307/249008.
   Web of Science ®Google Scholar
• Venkatesh V, Davis FD. A model of the antecedents of perceived ease of use: development and test. Decis. Sci 1996;27(3):451–81. doi:10.1111/j.1540-5915.1996.tb01822.x.
   Web of Science ®Google Scholar
• Rogers EM. The Diffusion of Innovations. 3rd ed. New York (NY): Free Press; 1983.
   Google Scholar
• Verma S, Bhattacharyya SS, Kumar S. An extension of the technology acceptance model in the big data analytics system implementation environment. Inf Process Manag. 2018;54(5):791–806. doi:10.1016/j.ipm.2018.01.004.
   Web of Science ®Google Scholar
• Dwivedi YK, Rana NP, Tamilmani K, Raman R. A meta-analysis based modified unified theory of acceptance and use of technology (meta-UTAUT): a review of emerging literature. Curr. Opin. Psychol 2020;36:13–18. doi:10.1016/j.copsyc.2020.03.008.
   PubMed Web of Science ®Google Scholar
• Lai PC. The literature review of technology adoption models and theories for the novelty technology. J. Inf. Syst. Technol. Manage 2017;14(1):21–38. doi:10.4301/S1807-17752017000100002.
   Google Scholar
• Oliveira T, Martins MF. Literature review of information technology adoption models at firm level. Electron. J. Inf. Syst. Eval 2011;14:110–21.
   Google Scholar
• Taherdoost H. A review of technology acceptance and adoption models and theories. Procedia Manuf 2018;22:960–67. doi:10.1016/j.promfg.2018.03.137.
   Google Scholar
• Granić A, Marangunić N. Technology acceptance model in educational context: a systematic literature review. Br J Educ Technol. 2019;50(5):2572–93. doi:10.1111/bjet.12864.
   Web of Science ®Google Scholar
• King WR, He J. A meta-analysis of the technology acceptance model. Inf. Manage 2006;43(6):740–55. doi:10.1016/j.im.2006.05.003.
   Web of Science ®Google Scholar
• Lee Y, Kozar KA, Larsen KR. The technology acceptance model: past, present, and future. Commun. Assoc. Inf. Syst 2003;12(1):752–80. doi:10.17705/1CAIS.01250.
   Google Scholar
• Legris P, Ingham J, Collerette P. Why do people use information technology? A critical review of the technology acceptance model. Inf. Manage 2003;40(3):191–204. doi:10.1016/S0378-7206(01)00143-4.
   Web of Science ®Google Scholar
• Scherer R, Siddiq F, Tondeur J. The technology acceptance model (TAM): a meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Comput Educ. 2019;128:13–35. doi:10.1016/j.compedu.2018.09.009.
   Web of Science ®Google Scholar
• SPSS. SPSS statistics for windows. Armonk (New York): IBM Knowledge Center; 2019.
   Google Scholar
• Malhotra MK, Grover V. An assessment of survey research in POM: from constructs to theory. J. Oper. Manag 1998;16(4):407–25. doi:10.1016/S0272-6963(98)00021-7.
   Google Scholar
• Yu J, Cooper H. A qualitative review of research design effects on response rates to questionnaires. J. Mark. Res 1983;20(1):36–44. doi:10.1177/002224378302000105.
   Web of Science ®Google Scholar
• Wagner SM, Kemmerling R. Handling non-response in logistics research. J. Bus. Logist 2010;31(2):357–81. doi:10.1002/j.2158-1592.2010.tb00156.x.
   Web of Science ®Google Scholar
• Armstrong JS, Overton TS. Estimating nonresponse bias in mail surveys. J. Mark. Res 1977;14(3):396–402. doi:10.1177/002224377701400320.
   Web of Science ®Google Scholar
• Kline RB. Principles and practice of structural equation modeling. 2nd. New York (NY): Guilford; 2005.
   Google Scholar
• Yuan KH, Bentler PM, Zhang W. The effect of skewness and kurtosis on mean and covariance structure analysis: the univariate case and its multivariate implication. Sociol Methods Res. 2005;34(2):240–58. doi:10.1177/0049124105280200.
   Web of Science ®Google Scholar
• Glamour C, Madigan D, Pregibon D, Smyth P. Statistical inference and data mining. Commun ACM. 1996;39(11):35–41. doi:10.1145/240455.240466.
   Web of Science ®Google Scholar
• Nachtigall C, Kroehne U, Funke F, Steyer R. Pros and cons of structural equation modeling. Meth. Psychol. Res. Online. 2003;8:1–22.
   Google Scholar
• Tomarken AJ, Waller NG. Structural equation modeling: strengths, limitations, and misconceptions. Annu Rev Clin Psychol. 2005;1(1):31–65. doi:10.1146/annurev.clinpsy.1.102803.144239.
   PubMed Web of Science ®Google Scholar
• Aboni J, Feil B. Cluster Analysis for Data Mining and System Identification. Basel (Switzerland): Springer Science; 2007.
   Google Scholar
• King RS. Cluster analysis and data mining: an introduction. Sterling (VA): Stylus Publishing, LLC; 2015.
   Google Scholar
• Okazaki S. What do we know about mobile Internet adopters? A cluster analysis. Inf. Manage 2006;43(2):127–41. doi:10.1016/j.im.2005.05.001.
   Web of Science ®Google Scholar
• Kanungo T, Mount DM, Netanyahu NS, Piatko CD, Silverman R, Wu AY. An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans Pattern Anal Mach Intell. 2002;24(7):881–92. doi:10.1109/TPAMI.2002.1017616.
   Web of Science ®Google Scholar
• Berry MJ, Linoff GS. Data mining techniques: for marketing, sales, and customer relationship management. New York (NY): John Wiley & Sons; 2004.
   Google Scholar
• Kothari RA, Dong MI. Decision trees for classification: a review and some new results. In: Pal SK, Pal A, editors. Pattern recognition: from classical to modern approaches. Singapore: World Scientific; 2001. p. 169–84.
   Google Scholar
• Priyam A, Abhijeeta GR, Rathee A, Srivastava S. Comparative analysis of decision tree classification algorithms. Int. J. Curr. Eng.Technol 2013;3:334–37.
   Google Scholar
• Lee SJ, Siau K. A review of data mining techniques. Ind. Manag. Data Syst 2001;101(1):41–46. doi:10.1108/02635570110365989.
   Web of Science ®Google Scholar
• Rokach L, Maimon OZ. Data mining with decision trees: theory and applications. 2nd. Singapore: World Scientific; 2015.
   Google Scholar
• Chen M, Han J, Yu PS. Data mining: another view from data base perspectives. IEEE Trans Knowl Data Eng. 1996;8(6):866–83. doi:10.1109/69.553155.
   Web of Science ®Google Scholar
• Quinlan JR. Induction of decision trees. Mach Learn. 1986;1(1):81–106. doi:10.1007/BF00116251.
   Google Scholar
• Camisón C, Villar-López A. Organizational innovation as an enabler of technological innovation capabilities and firm performance. J Bus Res. 2014;67(1):2891–902. doi:10.1016/j.jbusres.2012.06.004.
   Web of Science ®Google Scholar
• Chouki M, Talea M, Okar C, Chroqui R. Barriers to information technology adoption within small and medium enterprises: a systematic literature review. Int. J. Innov. Manag 2020;17(1):2050007. doi:10.1142/S0219877020500078.
   Web of Science ®Google Scholar
• Forés B, Camisón C. Does incremental and radical innovation performance depend on different types of knowledge accumulation capabilities and organizational size? J Bus Res. 2016;69(2):831–48. doi:10.1016/j.jbusres.2015.07.006.
   Web of Science ®Google Scholar
• Shefer D, Frenkel A. R&D, firm size and innovation: an empirical analysis. Technovation. 2005;25(1):25–32. doi:10.1016/S0166-4972(03)00152-4.
   Web of Science ®Google Scholar
• Dewar RD, Dutton JE. The adoption of radical and incremental innovations: an empirical analysis. Manage Sci. 1986;32(11):1422–33. doi:10.1287/mnsc.32.11.1422.
   Web of Science ®Google Scholar
• Moch MK, Morse EV. Size, centralization and organizational adoption of innovations. Am Sociol Rev. 1977;42(5):716–25. doi:10.2307/2094861.
   Web of Science ®Google Scholar
• Thong JY. An integrated model of information systems adoption in small business. J. Manag. Inf. Syst 1999;15(4):187–214. doi:10.1080/07421222.1999.11518227.
   Google Scholar
• Bharadwaj AS, Bharadwaj SG, Konsynski BR. Information technology effects on firm performance as measured by Tobin’s q. Manage Sci. 1999;45(7):1008–24. doi:10.1287/mnsc.45.7.1008.
   Web of Science ®Google Scholar
• Weiner BJ. A theory of organizational readiness for change. Implement. Sci 2009;4(1):67–75. doi:10.1186/1748-5908-4-67.
   PubMed Web of Science ®Google Scholar
• Wraikat H, Bellamy A, Tang H. Exploring organizational readiness factors for new technology implementation within non-profit organizations. Open J. Soc. Sci 2017;5(12):1–13. doi:10.4236/jss.2017.512001.
   Google Scholar
• Karahanna E, Straub DW, Chervany NL. Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Q. 1999;23(2):183–207. doi:10.2307/249751.
   Web of Science ®Google Scholar
• Triandis HC. Attitude and attitude change. New York (NY): John Wiley and Sons; 1971.
   Google Scholar
• Mathieson K. Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Inf. Syst. Res 1991;2(3):173–91. doi:10.1287/isre.2.3.173.
   Web of Science ®Google Scholar
• Venkatesh V, Morris MG. Why don’t men ever stop to ask for direction? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Q. 2000;24(1):115–39. doi:10.2307/3250981.
   Web of Science ®Google Scholar
• Attewell P. Technological diffusion and organizational learning: the case of business computing. Organ. Sci 1992;3(1):1–19. doi:10.1287/orsc.3.1.1.
   Web of Science ®Google Scholar
• Tarhini A, Arachchilage NAG, Abbasi MS. A critical review of theories and models of technology adoption and acceptance in information system research. IJTD. 2015;6(4):58–77. doi:10.4018/IJTD.2015100104.
   Google Scholar
• Kleinknecht A. Firm size and innovation. Small Bus. Econ 1989;1(3):215–22. doi:10.1007/BF00401858.
   Google Scholar
• Lefebvre LA, Harvey J, Lefebvre E. Technological experience and the technology adoption decisions in small manufacturing firms. R&D Manage 1991;21(3):241–49. doi:10.1111/j.1467-9310.1991.tb00761.x.
   Web of Science ®Google Scholar
• Cooper RB, Zmud RW. Information technology implementation research: a technological diffusion approach. Manage Sci. 1990;36(2):123–39. doi:10.1287/mnsc.36.2.123.
   Web of Science ®Google Scholar
• Premkumar G, Ramamurthy K, Nilakanta S. Implementation of electronic data interchange: an innovation diffusion perspective. J. Manag. Inf. Syst 1994;11(2):157–86. doi:10.1080/07421222.1994.11518044.
   Google Scholar
• Teo TS, Ranganathan C, Dhaliwal J. Key dimensions of inhibitors for the deployment of web-based business-to-business electronic commerce. IEEE Trans Eng Manage. 2006;53(3):395–411. doi:10.1109/TEM.2006.878106.
   Web of Science ®Google Scholar
• Teo TS, Ranganathan C. Adopters and non-adopters of business-to-business electronic commerce in Singapore. Inf. Manage 2004;42(1):89–102. doi:10.1016/j.im.2003.12.005.
   Web of Science ®Google Scholar
• Chau PY, Hui KL. Determinants of small business EDI adoption: an empirical investigation. J. Organ. Comput. Electron. Commer 2001;11(4):229–52. doi:10.1207/S15327744JOCE1104_02.
   Web of Science ®Google Scholar
• Zhu K, Dong S, Xu SX, Kraemer KL. Innovation diffusion in global contexts: determinants of post-adoption digital transformation of European companies. Eur J Inf Syst. 2006;15(6):601–16. doi:10.1057/palgrave.ejis.3000650.
   Web of Science ®Google Scholar
• Aldenderfer MS, Blashfield RK. Cluster analysis. Beverly Hills (CA): Sage Publications; 1984.
   Google Scholar
• Chae B, Olson D, Sheu C. The impact of supply chain analytics on operational performance: a resource-based view. Int. J. Prod. Res 2014;52(16):4695–710. doi:10.1080/00207543.2013.861616.
   Web of Science ®Google Scholar
• Conboy K, Mikalef P, Dennehy D, Krogstie J. Using business analytics to enhance dynamic capabilities in operations research: a case analysis and research agenda. Eur J Oper Res. 2020;281(3):656–72. doi:10.1016/j.ejor.2019.06.051.
   Web of Science ®Google Scholar
• Duan Y, Cao G, Edwards JS. Understanding the impact of business analytics on innovation. Eur J Oper Res. 2020;281(3):673–86. doi:10.1016/j.ejor.2018.06.021.
   Web of Science ®Google Scholar
• Zdravevski E, Lameski P, Apanowicz C, Ślȩzak D. From big data to business analytics: the case study of churn prediction. Appl Soft Comput. 2020;90:106164. doi:10.1016/j.asoc.2020.106164.
   Web of Science ®Google Scholar